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Name: Márk Antal Csizmadia

Type: User

Company: @sellpy

Bio: ML Engineer at Sellpy (SE), MSc Machine Learning at KTH Royal Institute of Technology (SE), BEng Electronic Engineering at the University of Manchester (UK)

Location: Stockholm, Sweden

Hello there!

MSc Embedded Systems Engineering BSc Electrical and Electronic Engineering

I am Márk Antal Csizmadia, a machine learning engineer at Sellpy. I graduated in 2022 at KTH Royal Institute of Technology in Stockholm, Sweden with a Master of Science degree in Machine Learning, and in 2020 from the University of Manchester with a Bachelor of Engineering in Electronic Engineering degree. I'm originally from Budapest, Hungary .

My goal is to use my knowledge of machine learning, deep learning, IoT, and cloud computing to work on solving exciting problems and to positively influence our future. While doing so, I enjoy learning new technologies and tools, and getting to know like-minded, motivated people on the way.

🙏 Selected Projects

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👐 Other Projects

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🔧 Tech Stack

📈 Stats

💯 My Dataset

I built and published an annotated object detection dataset titled Object Detection: Batteries, Dice, and Toy Cars. The annotated objects in the dataset include six-sided board game dice, AAA, AA, and 9 V batteries, toy cars, spoons, highlighters, and tea candles. The dataset was built through different means that included scraping images off the Internet with the Bing Image Search API, remixing existing datasets from the public domain, extracting video frames from videos downloaded from YouTube in line with its fair-use policy, and manually taking photographs. There are in overall 1644 images in the dataset that contain 2815 objects. I shared a starter notebook for exploring the dataset on Kaggle. The dataset Usability Score is 8.8 / 10.0.

📨 Contact

Márk Antal Csizmadia's Projects

dcgan-fake-faces icon dcgan-fake-faces

Deep convolutional generative adversarial networks (DCGANs) for generating fake faces with Tensorflow and Keras.

eigenfaces icon eigenfaces

Eigenfaces exercise. Started from here with ML.

em-simple icon em-simple

Expectation Maximization (EM) algorithm for estimating maximum likelihood (ML) parameters of partially observed data on a three-node Bayesian Network Probabilistic Graphical Model.

gitignore icon gitignore

A collection of useful .gitignore templates

hmm icon hmm

Hidden Markov Models (HMMs) for estimating the sequence of hidden states (decoding) via the Viterbi algorithm, and estimating model parameters (learning) via the Baum- Welch algorithm.

hopfield-networks icon hopfield-networks

Implementing Hopfield Networks from scratch, testing their content addressable memory, attractor, and energy landscape, investigating their resistance to noise, experimenting with their memory capacity, and putting strain on them with sparse patterns.

hyperball-hyperloglogcounters icon hyperball-hyperloglogcounters

Implementation of the HyperBall algorithm with HyperLogLogCounters from the paper titled "In-Core Computation of Geometric Centralities with HyperBall: A Hundred Billion Nodes and Beyond".

naive_cnn icon naive_cnn

Naive implementation of Convolution Neural Networks (CNNs). Example architecture LeNet5 on MNIST hand-written digits.

nn-blocks icon nn-blocks

A neural network library built from scratch, without dedicated deep learning packages. Training and testing deep neural networks and utilizing deep learning best practices for multi-class classification with fully connected neural networks, text generation with recurrent neural networks, and regression with fully connected networks.

pca-mds-isomap icon pca-mds-isomap

Dimensionality reduction and data embedding via PCA, MDS, and Isomap.

personal-website icon personal-website

My personal website built with Django. An absolute overkill ... but good practice. Will set up a normal portfolio website with Github pages.

rbm_dbn icon rbm_dbn

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) from scratch for representation learning on the MNIST dataset.

re-sln icon re-sln

Re-implementation of the paper titled "Noise against noise: stochastic label noise helps combat inherent label noise" from ICLR 2021.

shields icon shields

Concise, consistent, and legible badges in SVG and raster format

slp-mlp icon slp-mlp

Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.

som icon som

Kohonen Self-Organizing Maps (SOMs) for dimensionality reduction, data embedding, and solving a variant of the travelling salesman problem.

spectral-clustering icon spectral-clustering

Re-implementation of the paper titled "On Spectral Clustering: Analysis and an algorithm" by AY Ng et al.

svm icon svm

Support Vector Machines (SVMs) from scratch, without dedicated packages, for the classification of linear and non-linear data.

variational-inference icon variational-inference

Factorized variational approximation using a univariate Gaussian distribution over a single variable x.

variational-inference-gmm icon variational-inference-gmm

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.

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